In order to measure vibration, the rotorcraft is fitted with accelerometers that are placed (secured) on the casing(s) of the gearbox(es), on the bearings of shafts, and/or on other points of the structure of the helicopter.
In flight, the signals delivered by these sensors can be converted into data, and where appropriate synchronized (by using signals delivered by a rotation sensor), and/or “averaged”, and then recorded by the imbedded system.
On returning to the ground, the recorded data can be collated and analyzed. Interpreting such data is complex: it requires lengthy intervention by an expert.
Known tools for automatically analyzing such data in order to diagnose a mechanical defect in a mechanism are incomplete and imperfect; there are existing defects which such tools fail to detect, and they sometimes generate indications of a defect when none is justified.
An object of the invention is to provide a method of analyzing such data, an analysis program, and a device including the program, making it possible to draw up quickly a diagnosis that is reliable, i.e. that maximizes the percentage of real defects detected while minimizing the percentage of defect detections that are not confirmed.
A rotorcraft rotor, in particular a propulsion and lift rotor of a helicopter, conventionally comprises a plurality of mechanical members that are adjustable or displaceable, referred to as adjustment members, having setting or configuration values that have a considerable influence on the vibration produced while the rotorcraft is in operation; these members include balance flyweights secured to each blade or to each structure (sleeve) for securing a blade to the rotor hub and of mass that can be adjusted, tabs secured to each blade and of orientation that can be modified, and members for adjusting the lengths of pitch control rods respectively associated with each of the blades.
Among the defects of mechanical elements of a rotor that have an influence on a vibratory signature of the helicopter, mention can be made of slack in bearings and in fastenings, and also to degraded mechanical characteristics of a part due to aging, such as a change in its stiffness or in the damping provided by a lag damper, for example.
These adjustment means can be used for adjusting the respective resonant frequencies of the blades corresponding to their second mode in flapping, as described in patent U.S. Pat. No. 6,311,924.
Patents WO-02/090903, US-2005-125103, and US-2006-058927 describe methods of detecting adjustment errors or defects of a rotorcraft rotor in order to adjust the adjustment members so as to minimize vibration levels.
In those methods, a neural network is used illustrative of the relationship between accelerations (vibrations) and adjustment parameters or defects. The acquisition of vibration measurements from which vibratory signatures are calculated generally requires flights to be performed in the presence of defective mechanical elements on the rotor in various different configurations of defects and adjustment errors: a properly adjusted defect-free rotor; a rotor without any defects but with adjustment error concerning its flyweights, its pitch rods, and/or its tabs; and a rotor without adjustment error but including a defective member.
An artificial neural network (ANN) is a calculation model of design inspired on the operation of biological neurons, and it is generally optimized by a statistical type learning method. An ANN generally comprises a multitude of calculation units of identical structure referred to as artificial neurons (AN) that are organized in layers.
Each AN is generally characterized by synaptic weights, a combination or aggregation function (e.g. summing) of inputs weighted by the synaptic weights, and an activation function that defines the output from the neuron as a function of the result of the combination function when compared with a threshold.
Each ANN is characterized in particular by the topology of the connections between its neurons, the types of combination and activation functions, and by the learning method, i.e. iterative modification of the values of the synaptic weights.
These methods include supervised learning methods in which the ANN is forced to converge on a predefined state or output, and non-supervised methods.
Among such methods, a distinction is also made between competitive learning methods in which only a fraction of the weights are modified during an iteration, i.e. only the weights of a “winning” or “elected” neuron, possibly together with the weights of neurons close to the elected neuron.
A self-organized map (SOM), or Kohonen map, is a particular ANN generally comprising a single layer of neurons with a binary output (in particular equal to zero or one), and in which non-supervised learning is competitive: at each instant, only one neuron, in theory, is “active” or “elected”, i.e. the neuron having weights that are the closest to the input data under consideration.
The documents “Diagnostic de défauts des rotors d'hélicoptères: approches connexionnistes pour l'analyse vibratoire” [Diagnosing helicopter rotor defects: connectionist approaches for vibratory analysis] by H. Morel, in Rapport DEA Modélisation et conception Assistée par Ordinateur, published by Ecole Supérieure des Arts et Métiers, UMR CNRS 6168, 2003, and “Defect detection and tracing on helicopter rotor by artificial neural networks” by H. Morel et al., Advanced Process control Applications for Industry Workshop, APC2005, IEEE, Vancouver, 2005, propose using self-organizing maps to diagnose helicopter rotor defects.